Omicron BA.2 Prediction Research Based on SEIR-ARIMA Mixed Model

Abstract

Omicron BA.2, a new variant of severe acute respiratory syndrome coronavirus (SARS-CoV-2), has attracted worldwide attention due to its high infectivity and vaccine escape mutation. Based on the SEIR model being susceptible to changes in external factors and having specific errors, the ARIMA model is data-dependent and can only capture linear relationships. In this paper, based on the traditional infectious disease dynamic model SEIR and the differential integrated mean autoregressive model ARIMA, an SEIR-ARIMA mixed model is proposed to predict and evaluate the virus outbreak in March in Jilin Province, China. The data from SEIR and ARIMA models were processed using SPSS to obtain the predicted values f and e, respectively. Linear regression modeling was performed on the predicted values f and e to establish the SEIR-ARIMA model. MATLAB is used to complete the best linear fitting line. Furthermore, The results show that the model's predicted value is in good agreement with the actual value. It shows that the SEIR-ARIMA mixed model based on the SEIR-ARIMA model has a good prediction effect, which is beneficial for the country to make the right decision when facing the epidemic. It is of great value for preventing other types of infectious diseases in China in the future.